DTW aligns two sequences by taking the point-to-point comparison of
coordinate values as a fundamental unit [1]}.
Concretely, one point on a sequence is compared to all or a subset of points
on the other sequence to compute a point level distance. In doing so, one
can find an alignment path such that the aligned sequences yields a globally
minimum Euclidean distance [2]}, which is returned as the
distance between the original sequences. However, this point-level
comparison usually cannot provide dynamic evidence for matching two
points. Note that from human's intuition, we intend to pair two points of
two sequences by taking into account the nearby points or even the global
structure. Thus, DTW does align globally but is unable to take into
consideration the auto-correlated structure information properly [3]}.
This makes DTW fragile to noise [4]}, which is also demonstrated in our
experiment (Section ). As pointed
out in [3]}, [6]}, DTW usually has weak
interpretability of its alignment. Concretely, the alignment result may lack
local semantic meanings. For example, DTW may match points on a local peak
and a valley if their Euclidean distance is small. Besides, DTW may match
one point to too many points resulting in un-intuitive alignment results and
degrade the classification performance [7]}.
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